Skip to main content

Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics

Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

The objective of this paper is to present an occupant detection method through step-induced structural vibration. Occupant detection enables various smart building applications such as space/energy management. Ambient structural vibration monitoring provides a non-intrusive sensing approach to achieve that. The main challenges for structural vibration based occupant footstep detection include that (1) the ambient structural vibration noise may overwhelm the step-induced vibration and (2) there are various other impulse-like excitations that look similar to footstep excitations in the sensing environment (e.g., door closing, chair dragging, etc.), which increase the false alarm rate for occupant detection. To overcome these challenges, a two-stage step-induced signal detection algorithm is developed to (1) incorporate the structural characteristics by selecting the dominant frequencies of the structure to increase the signal-to-noise ratio in the vibration data and thus improve the detection performance and (2) perform footstep classification on detected events to distinguish step-induced floor vibrations from other impulse excitations. The method is validated experimentally in two different buildings with distinct structural properties and noise characteristics, Carnegie Mellon University (CMU) campus building and Vincentian Nursing Home deployments in Pittsburgh, PA. The occupant footstep detection F1 score shows up to 4X reduction in detection error compared to traditional thresholding method.

Keywords

  • Occupant detection
  • Structural vibration
  • Wavelet analysis
  • One-class classification
  • Natural frequency

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-29763-7_35
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-29763-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.99
Price excludes VAT (USA)
Hardcover Book
USD   349.99
Price excludes VAT (USA)
Fig. 35.1
Fig. 35.2
Fig. 35.3
Fig. 35.4
Fig. 35.5

References

  1. Teixeira, T., Dublon, G., Savvides, A.: A survey of human-sensing: methods for detecting presence, count, location, track, and identity. ACM Comput. Surv. 5, 1–77 (2010)

    Google Scholar 

  2. Ekimov, A., Sabatier, J.M.: Vibration and sound signatures of human footsteps in buildings. J. Acoust. Soc. Am. 120(2), 762–768 (2006)

    CrossRef  Google Scholar 

  3. Jin, X., Sarkar, S., Ray, A., Gupta, S., Damarla, T.: Target detection and classification using seismic and PIR sensors. IEEE Sensors J. 12(6), 1709–1718 (2012)

    CrossRef  Google Scholar 

  4. Sun, Z., Pan, S., Su, Y.-C., Zhang, P.: Headio: zero-configured heading acquisition for indoor mobile devices through multimodal context sensing. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 33–42. ACM, New York (2013)

    Google Scholar 

  5. Sun, Z., Purohit, A., Chen, K., Pan, S., Pering, T., Zhang, P.: PANDAA: physical arrangement detection of networked devices through ambient-sound awareness. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 425–434. ACM, New York (2011)

    Google Scholar 

  6. Sun, Z., Purohit, A., Bose, R., Zhang, P.: Spartacus: spatially-aware interaction for mobile devices through energy-efficient audio sensing. In: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 263–276. ACM, New York (2013)

    Google Scholar 

  7. Nunes, D.S, Zhang, P., Silva, J.S.: A survey on human-in-the-loop applications towards an internet of all. IEEE Commun. Surv. Tutorials 17(2), 944–965 Secondquarter (2015)

    Google Scholar 

  8. Purohit, A., Sun, Z., Pan, S., Zhang, P.: Sugartrail: indoor navigation in retail environments without surveys and maps. In: 10th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2013, pp. 300–308. IEEE, New York (2013)

    Google Scholar 

  9. Mirshekari, M., Pan, S., Bannis, A., Pui, Y., Lam, M., Zhang, P., Noh, H.Y.: Step-level person localization through sparse sensing of structural vibration. In: Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 376–377. ACM, New York (2015)

    Google Scholar 

  10. Pan, S., Bonde, A., Jing, J., Zhang, L., Zhang, P., Noh, H.Y.: Boes: building occupancy estimation system using sparse ambient vibration monitoring. In SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring, pp. 90611O–90611O. International Society for Optics and Photonics (2014)

    Google Scholar 

  11. Pan, S., Wang, N., Qian, Y., Velibeyoglu, I., Noh, H.Y., Zhang, P.: Indoor person identification through footstep induced structural vibration. In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, pp. 81–86. ACM, New York (2015)

    Google Scholar 

  12. Subramanian, A., Mehrotra, K.G., Mohan, C.K., Varshney, P.K., Damarla T.: Feature selection and occupancy classification using seismic sensors. In: Trends in Applied Intelligent Systems, pp. 605–614. Springer, Berlin (2010)

    Google Scholar 

  13. Bland, R.E.: Acoustic and seismic signal processing for footstep detection. Ph.D. thesis, Massachusetts Institute of Technology (2006)

    Google Scholar 

  14. Alyamkin, S.A., Eremenko, S.I.: Pedestrian detection algorithms based on an analysis of the autocorrelation function of a seismic signal. Optoelectronics Instrum. Data Process. 47(2), 124–129 (2011)

    CrossRef  Google Scholar 

  15. Succi, G.P., Clapp, D., Gampert, R., Prado, G.: Footstep detection and tracking. In: Aerospace/Defense Sensing, Simulation, and Controls, pp. 22–29. International Society for Optics and Photonics (2001)

    Google Scholar 

  16. Koç, G., Yegin, K.: Footstep and vehicle detection using slow and quick adaptive thresholds algorithm. Int. J. Distrib. Sens. Netw. 2013, 9 (2013). doi:10.1155/2013/783604

    Google Scholar 

  17. Houston, K.M., McGaffigan, D.P.: Spectrum analysis techniques for personnel detection using seismic sensors. In: AeroSense 2003, pp. 162–173. International Society for Optics and Photonics (2003)

    Google Scholar 

  18. Xing, H.-F., Li, F., Liu, Y.-L.: Wavelet denoising and feature extraction of seismic signal for footstep detection. In: ICWAPR’07. International Conference on Wavelet Analysis and Pattern Recognition, 2007, vol. 1, pp. 218–223. IEEE, New York (2007)

    Google Scholar 

  19. Ripul Ghosh, Aparna Akula, Satish Kumar, and HK Sardana. Time-frequency analysis based robust vehicle detection using seismic sensor. J. Sound Vib. 346, 424–434 (2015)

    Google Scholar 

  20. Huang, J., Zhou, Q., Zhang, X., Song, E., Li, B., Yuan, X.: Seismic target classification using a wavelet packet manifold in unattended ground sensors systems. Sensors 13(7), 8534–8550 (2013)

    Google Scholar 

  21. Noh, H.Y., Nair, K.K., Lignos, D.G., Kiremidjian, A.S.: Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data. J. Struct. Eng. 137(10), 1215–1228 (2011)

    CrossRef  Google Scholar 

  22. Noh, H., Kiremidjian, A.S.: On the use of wavelet coefficient energy for structural damage diagnosis. In: Proceedings of the10th International Conference on Structural Safety and Reliability, Osaka (2009)

    Google Scholar 

  23. Noh, H.Y., Lignos, D., Nair, K.K., Kiremidjian, A.S.: Application of wavelet based damage sensitive features for structural damage diagnosis. In: Proceedings of the 7th International Workshop on Structural Health Monitoring (2009)

    Google Scholar 

  24. Ling, T.-H., Li, X.-B.: Analysis of energy distributions of millisecond blast vibration signals using the wavelet packet method. Chin. J. Rock Mech. Eng. 24(7), 1117–1122 (2005)

    Google Scholar 

  25. David, M.J.: Tax. one-class classification; concept-learning in the absence of counter-examples. ASCI Dissertation Series, 65 (2001)

    Google Scholar 

  26. Khan, S.S., Madden, M.G.: One-class classification: taxonomy of study and review of techniques. Knowl. Eng. Rev. 29(03), 345–374 (2014)

    Google Scholar 

  27. Khan, S.S., Madden, M.G.: A survey of recent trends in one class classification. In Artificial Intelligence and Cognitive Science, pp. 188–197. Springer, Berlin (2010)

    Google Scholar 

  28. Chang, C.-C., Lin, C.-J.: Training v-support vector classifiers: theory and algorithms. Neural Comput. 13(9), 2119–2147 (2001)

    CrossRef  MATH  Google Scholar 

  29. Schölkopf, B., et al.: Support vector method for novelty detection. NIPS. 12 (1999)

    Google Scholar 

  30. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)

    CrossRef  MATH  Google Scholar 

  31. Preece, S.J., Goulermas, J.Y., Kenney, L.P.J., Howard, D., Meijer, K., Crompton, R.: Activity identification using body-mounted sensors a review of classification techniques. Physiol. Meas. 30(4), R1(2009)

    Google Scholar 

  32. Li, K.-L., Huang, H.-K., Tian, S.-F., Xu, W.: Improving one-class SVM for anomaly detection. In: 2003 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 3077–3081. IEEE, New York (2003)

    Google Scholar 

  33. Shin, H.J., Eom, D.-H., Kim, S.-S.: One-class support vector machines an application in machine fault detection and classification. Comput. Ind. Eng. 48(2), 395–408 (2005)

    CrossRef  Google Scholar 

  34. Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2, 139–154 (2002)

    MATH  Google Scholar 

  35. Rabaoui, A., Davy, M., Rossignol, S., Lachiri, Z., Ellouze, N.: Improved one-class SVM classifier for sounds classification. In: IEEE Conference on Advanced Video and Signal Based Surveillance, 2007. AVSS 2007, pp. 117–122. IEEE, New York (2007)

    Google Scholar 

  36. Zhou, J, Chan, K.L., Chong, V.F.H., Krishnan, S.M.: Extraction of brain tumor from mr images using one-class support vector machine. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005, pp. 6411–6414. IEEE, New York (2006)

    Google Scholar 

  37. Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)

    CrossRef  MATH  Google Scholar 

  38. Ren, W.-X., Zong, Z.-H.: Output-only modal parameter identification of civil engineering structures. Struct. Eng. Mech. 17(3–4), 429–444 (2004)

    CrossRef  Google Scholar 

  39. Edwards, M., Xie, X.: Footstep pressure signal analysis for human identification. In: 2014 7th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 307–312. IEEE, New York (2014)

    Google Scholar 

  40. Sabatier, J.M., Ekimov, A.E.: A review of human signatures in urban environments using seismic and acoustic methods. In: 2008 IEEE Conference on Technologies for Homeland Security, pp. 215–220. IEEE, New York (2008)

    Google Scholar 

  41. I/O Sensor Nederland bv. SM-24 Geophone Element, 2006. P/N 1004117

    Google Scholar 

  42. Aggarwal, C.C.: Outlier Analysis. Springer Science & Business Media, Berlin (2013)

    CrossRef  MATH  Google Scholar 

  43. Hodge, V.J., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

    Google Scholar 

Download references

Acknowledgements

This work is partially supported by National Science Foundation (NSF) under awards CNS-1149611, Pennsylvania Infrastructure Technology Alliance (PITA), CMU-SYSU Collaborative Innovation Research Center (CIRC), Intel, Nokia, and Renault. The authors would also like to acknowledge Vincentian Nursing Home for providing deployment sites to conduct experiments and collect data.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 The Society for Experimental Mechanics, Inc.

About this paper

Cite this paper

Lam, M., Mirshekari, M., Pan, S., Zhang, P., Noh, H.Y. (2016). Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics. In: Allen, M., Mayes, R., Rixen, D. (eds) Dynamics of Coupled Structures, Volume 4. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29763-7_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29763-7_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29762-0

  • Online ISBN: 978-3-319-29763-7

  • eBook Packages: EngineeringEngineering (R0)